{"id":"W2037961110","doi":"10.1142/s0218126610006724","title":"REAL-TIME STEGANALYSIS OF LSB-REPLACEMENT IN DIGITAL AUDIO STREAMS","year":2010,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; McMaster University","funders":"","keywords":"Steganalysis; Computer science; Steganography; Least significant bit; Histogram; Digital audio; Embedding; Sliding window protocol; Volume (thermodynamics); Transmission (telecommunications); Data stream; Computer hardware; Audio signal; Real-time computing; Artificial intelligence; Image (mathematics); Window (computing); Digital signal processing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002251573,0.0002552407,0.0001909823,0.0003268076,0.000100988,0.0001719246,0.0002002541,0.0002022065,0.0004671083],"category_scores_gemma":[0.001001325,0.0001000056,0.0001364396,0.0001604243,0.0002854828,0.0003347898,0.0001639825,0.0001723412,0.000125873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001796132,"about_ca_system_score_gemma":0.0001620246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004056758,"about_ca_topic_score_gemma":0.0006413516,"domain_scores_codex":[0.9998831,0.00002139184,0.000005851673,0.00001912438,0.00005774881,0.00001293051],"domain_scores_gemma":[0.9996176,0.0002215807,0.00006201689,0.00003571677,0.00005416224,0.0000089051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006687008,0.0001084278,0.003777994,0.0001696481,0.00004166244,0.0003502001,0.0001846486,0.05024464,0.717771,0.005106512,0.0003251765,0.2212514],"study_design_scores_gemma":[0.00002292595,0.0002756515,0.002922198,0.00001917498,0.00001665389,0.0005508928,0.00003551952,0.557286,0.4370145,0.001012201,0.000828251,0.00001593888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5916457,0.0003356353,0.4054364,0.0001093004,0.00004644074,0.00003554886,0.00003502697,0.0008413571,0.001514558],"genre_scores_gemma":[0.9468609,0.0001743275,0.0519881,0.00001696262,0.00001056333,0.00001307639,0.00002461291,0.00002009514,0.0008913076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004671083,"threshold_uncertainty_score":0.001562655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006637844560632907,"score_gpt":0.221157670392912,"score_spread":0.2145198258322791,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}